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Cybersecurity Solutions for Wireless Sensor Networks in IoT Environments

Submission Deadline: 05 February 2024 (closed) Submit to Special Issue

Guest Editors

Dr. Anwar Ghani, International Islamic University, Pakistan
Dr. Shehzad Ashraf Chaudhry, Abu Dhabi University, United Arab Emirates; Nisantasi University, Turkey
Dr. Rashid Ahmad, COMSATS University Islamabad, Pakistan


In the vast environment of the Internet of Things (IoT), wireless sensor networks (WSNs) are considered as the most important and robust cybersecurity solutions. Safeguarding sensitive data and fending off potential threats require proactive measures in the interconnected world of IoT. Here we are aiming to explore effective cybersecurity solutions tailored specifically for WSNs in IoT environments. Normally, secure communication protocols are vital to protect data transmitted within WSNs. Protocols like Transport Layer Security (TLS) or Secure Shell (SSH) provide encryption and authentication ensuring data confidentiality and integrity during transmission. In Addition, strong authentication mechanisms such as two factor authentication or digital certificates are used to validate device identities and prevent unauthorized access. Hence, implementing access control policies limits privileges allowing only authorized entities to interact with the network.


Data encryption serves as a critical component of a robust cybersecurity strategy. Encryption algorithms like Advanced Encryption Standard (AES) secure data at rest and in transit, rendering it indecipherable without the corresponding decryption key. Since Intrusion detection systems (IDS) play a crucial role in identifying and mitigating security breaches in real-time by monitoring network traffic and analysing patterns. Regularly updating firmware and software, along with physical security measures like tamper-evident seals are used further enhance the security position. Continuous monitoring and incident response capabilities, supported by network monitoring tools and security information and event management (SIEM) systems, ensure prompt detection and coordinated response to security incidents.


Further, implementing these tailored cybersecurity solutions, organizations can strengthen their defences against potential threats in WSNs within IoT environments. Remaining vigilant, staying abreast of emerging security practices, and adapting to evolving threats are key to maintaining a secure IoT ecosystem. In this special issue we seek studies that propose novel cryptographic algorithms, secure communication protocols, intrusion detection and prevention systems, privacy-enhancing techniques, and other cutting-edge security solutions tailored specifically for WSNs in IoT applications.


List of topics include but not limited to the following:

● Lightweight cryptographic algorithms for resource-constrained WSNs

● Secure communication protocols for IoT devices and gateways

● Intrusion detection and prevention systems for WSNs in IoT applications

● Privacy-preserving techniques for data collection and transmission in WSNs

● Machine learning and AI-based anomaly detection in WSNs

● Blockchain-enabled security solutions for WSNs in IoT environments

● Context-aware security mechanisms for dynamic IoT environments

● Secure key management schemes for WSNs

● Authentication and access control in IoT systems

● Resilience and fault tolerance in WSNs against cyber-physical attacks

● Trust and reputation management in IoT environments

● Security architectures and frameworks for WSNs in IoT applications


Cyber Security;
Internet of Things (IoT);
Wireless sensor networks;
Artificial Intelligence (AI);
Machine learning

Published Papers

  • Open Access


    Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

    Cai Ming Liu, Yan Zhang, Zhihui Hu, Chunming Xie
    CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2023.045282
    (This article belongs to this Special Issue: Cybersecurity Solutions for Wireless Sensor Networks in IoT Environments )
    Abstract Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods. This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method. The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements. Then, to improve the accuracy of similarity calculation, a quantitative matching method is proposed. The model uses mathematical methods to train and evolve immune elements, increasing the diversity of… More >

  • Open Access


    Developing Transparent IDS for VANETs Using LIME and SHAP: An Empirical Study

    Fayaz Hassan, Jianguo Yu, Zafi Sherhan Syed, Arif Hussain Magsi, Nadeem Ahmed
    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3185-3208, 2023, DOI:10.32604/cmc.2023.044650
    (This article belongs to this Special Issue: Cybersecurity Solutions for Wireless Sensor Networks in IoT Environments )
    Abstract Vehicular Ad-hoc Networks (VANETs) are mobile ad-hoc networks that use vehicles as nodes to create a wireless network. Whereas VANETs offer many advantages over traditional transportation networks, ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks. This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System (IDS) that merges Machine Learning (ML)–based attack detection with Explainable AI (XAI) explanations. This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest (RF) classifier that achieves a classification accuracy of 100% for the binary classification task… More >

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